Decision-making framework with double-loop learning through interpretable black-box machine learning models. Issue 7 (14th August 2017)

• Record Type: Journal Article
• Authors: Bohanec, Marko; Robnik-Šikonja, Marko; Kljajić Borštnar, Mirjana
• Is Part Of: Industrial management & data systems. Volume 117:Issue 7(2017)
• Page Start: 1389
• Page End: 1406
• Publication Date: 2017-08-14
• Subjects: Machine learning -- Double-loop learning -- B2B sales forecasting -- Explanation of black-box models; Industrial management -- Periodicals; Electronic data processing -- Periodicals; Business -- Periodicals; Industrial management -- Great Britain -- Periodicals; 658.05
• DOI: 10.1108/IMDS-09-2016-0409
• Languages: English
• ISSNs: 0263-5577

Abstract

Abstract : Purpose: The purpose of this paper is to address the problem of weak acceptance of machine learning (ML) models in business. The proposed framework of top-performing ML models coupled with general explanation methods provides additional information to the decision-making process. This builds a foundation for sustainable organizational learning. Design/methodology/approach: To address user acceptance, participatory approach of action design research (ADR) was chosen. The proposed framework is demonstrated on a B2B sales forecasting process in an organizational setting, following cross-industry standard process for data mining (CRISP-DM) methodology. Findings: The provided ML model explanations efficiently support business decision makers, reduce forecasting error for new sales opportunities, and facilitate discussion about the context of opportunities in the sales team. Research limitations/implications: The quality and quantity of available data affect the performance of models and explanations. Practical implications: The application in the real-world company demonstrates the utility of the approach and provides evidence that transparent explanations of ML models contribute to individual and organizational learning. Social implications: All used methods are available as an open-source software and can improve the acceptance of ML in data-driven decision making. Originality/value: The proposed framework incorporates existing ML models and general explanation methodology into a decision-making process. To the authors' knowledge, this is the first attempt to support organizational learning with a framework combining ML explanations, ADR, and data mining methodology based on the CRISP-DM industry standard.